Best MLOps Certifications To Boost Your Career In 2024

Knowing how to integrate machine learning into operational workflows has become a must have skill for data scientists. With the increasing demand for skilled MLOps practitioners , various MLOps certifications have emerged to help professionals validate their expertise. This blog explores the top MLOps certifications, training courses, and the best resources to help you prepare for this journey. 

Learn to Build an End-to-End Machine Learning Pipeline - Part 1

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With the global Machine Learning Operations (MLOps) market size likely to reach USD75.42 billion by 2033, the demand for MLOps professionals will skyrocket in the next few years. Imagine being at the forefront of transforming raw data into actionable insights, seamlessly deploying and managing machine learning models. So, if you are looking for a job with a future brighter than a thousand LED lights, MLOps might be your perfect match. This blog is the perfect guide to exploring the secrets of this booming field, from understanding the hottest MLOps certifications to getting hands-on experience with real-world MLOps project examples. 

Why You Must Pursue MLOps Certifications?

From gaining a competitive edge in the industry to mastering the art of deploying ML models, there are several reasons to pursue an MLOps course or certification. Let us look at a few essential reasons why an MLOps course or certification is your key to standing out in the data science and machine learning industry-

1. Better Credibility

MLOps certifications validate your skills, providing employers a clear understanding of your capabilities in deploying and managing ML models. For instance, professionals who have received the AWS MLOps certification online training are highly demanded for their proven expertise in designing, implementing, and maintaining robust ML solutions on the AWS platform.

2. Overall Career Growth

The increasing adoption of ML technologies across industries has generated a high demand for professionals with MLOps expertise. Companies like Microsoft are actively recruiting individuals with Azure MLOps certifications, showcasing the industry's recognition of the certification's value. 

Furthermore, since MLOps courses and certifications are recognized by top tech companies, this further enhances your resume and increases your earning potential. For instance, the average annual salary for MLOps engineers is $1,20,715 in the US, which can grow higher by pursuing any relevant MLOps course and certification.

3. Competitive Edge in The Job Market

MLOps courses and certifications help you stand out in a competitive job market, offering a strategic advantage over non-certified competitors. Job listings for data/software engineering roles often prioritize candidates with GCP Professional Machine Learning Engineer certification, indicating its impact on the hiring process.

4. Hands-on Learning

MLOps courses and certifications often involve hands-on MLOps projects that simulate real-world challenges, enhancing practical problem-solving and data management skills. For instance, professionals pursuing the Databricks MLOps certification showcase their ability to manage complex data scenarios, leading to improved decision-making.

Let us move on to the top MLOps courses and certifications you should consider pursuing in 2024 to learn MLOps and boost your data science career.

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Top MLOps Certifications To Pursue in 2024

This section explores the top MLOps courses and certifications you need to become an MLOps pro. Before moving on to the certifications, here’s an overview of the MLOps learning plan for you, as shared by Benjamin Rogojan, Data Science And Data Engineering Consultant at Acheron Analytics- 

Source: Medium

1. AWS MLOps Certification

The AWS MLOps certification stands out as a key credential for professionals aiming to master the intersection of machine learning and operations within the AWS environment. A notable component of this certification is the 'MLOps Engineering on AWS' classroom training, designed to offer a comprehensive understanding of deploying and managing ML models effectively on cloud platforms.

New Projects

Databricks Data Lineage and Replication Management

Many-to-One LSTM for Sentiment Analysis and Text Generation

Many-to-One LSTM for Sentiment Analysis and Text Generation

Llama2 Project for MetaData Generation using FAISS and RAGs

Stock Price Prediction Project using LSTM and RNN

Learn to Build an End-to-End Machine Learning Pipeline - Part 1

Build an ETL Pipeline for Financial Data Analytics on GCP-IaC

Many-to-One LSTM for Sentiment Analysis and Text Generation

MLOps Project to Build Search Relevancy Algorithm with SBERT

dbt Snowflake Project to Master dbt Fundamentals in Snowflake

• MLOps Engineering on AWS 

‘MLOps Engineering on AWS’ is a 3-day immersive classroom training program designed to equip you with the practical skills and knowledge to build, train, and deploy ML models on the AWS platform. Whether you are a data scientist, software engineer, or operations professional, this MLOps course will give you the edge you need to excel in the booming MLOps field.

Suitable Candidates

• This MLOps course is ideal for individuals with a basic understanding of AWS and machine learning concepts who want to transition into MLOps roles.

• This course will be valuable for data scientists, DevOps engineers, and software developers looking to enhance their MLOps skills.

Prerequisites

• AWS Technical Essentials course (classroom or digital)

• ‘DevOps Engineering on AWS’ course or equivalent experience

• ‘Practical Data Science with Amazon SageMaker’ course or equivalent experience

Course Curriculum

• MLOps Fundamentals- Understand the challenges and best practices of the ML model lifecycle management.

• AWS MLOps Tools and Services- Deep dive into SageMaker, CloudFormation, CodeBuild, and other key tools for automation and orchestration.

• Hands-on Lab Exercises- Build and deploy your ML pipeline, gaining practical experience with Continuous Integration/Continuous Delivery workflows, monitoring, and model versioning.

• Troubleshooting and Optimization- Learn best practices for model performance optimization and identifying and resolving deployment issues.

Skills Acquired

• Designing and implementing MLOps pipelines for efficient model development and deployment.

• Automating model training, testing, and deployment using AWS services.

• Monitor and manage ML models in production, ensuring performance and reliability.

• Collaborating effectively with data scientists, developers, operations, and ML teams.

Once you have completed the MLOps Engineering on AWS training course, the next step is to enroll yourself in the AWS Machine Learning- Specialty certification.

• AWS Machine Learning- Specialty

Wondering how to validate your expertise in building and managing ML systems and workloads on the AWS cloud? Look no further than the AWS Machine Learning - Specialty Certification. This certification assesses your ability to design, implement, optimize, and continuously monitor ML solutions on AWS, making you a highly sought-after talent in the booming MLOps market.

Suitable Candidates

• This certification is ideal for individuals with a strong background in machine learning who want to specialize in leveraging AWS services for ML applications.

• It is suitable for data scientists, ML engineers, and developers with at least two years of experience building, architecting, or running ML/deep learning workloads in the AWS cloud.

Prerequisites

• Solid understanding of ML algorithms, data science concepts, and at least one deep learning framework.

• Practical experience with AWS services like SageMaker, Kinesis, Lambda, and CloudFormation.

• Familiarity with Continuous Integration/Continuous Delivery (CI/CD) practices and DevOps principles.

Course Highlights

• ML Fundamentals- Algorithms, model evaluation, optimization techniques, and bias mitigation.

• AWS ML Services- Deep dive into SageMaker, Amazon Elasticsearch Service, Kinesis, and other relevant tools.

• Building and Training ML Models- Data preprocessing, model selection, hyperparameter tuning, and training methodologies.

• Deployment and Monitoring- Automating model deployment, monitoring performance, and managing ML and data pipelines.

• Security and Governance- Implementing security best practices and ensuring compliance with regulations.

Skills Acquired

• In-depth knowledge of architecting and deploying ML solutions using AWS services.

• Proficiency in selecting the right AWS services for different ML scenarios.

• Expertise in optimizing models for performance and scalability on the AWS infrastructure.

2. Azure MLOps Certification

This Azure MLOps section discusses the comprehensive training in end-to-end machine learning operations using Azure Machine Learning and then explores the Microsoft Azure Data Scientist Associate (DP-100) certification. This MLOps course and certification equip you with the skills to effectively deploy and manage machine learning models on the Azure platform, opening doors to exciting opportunities in data science and machine learning.

• End-to-End Machine Learning Operations (MLOps) with Azure Machine Learning

The End-to-End Machine Learning Operations (MLOps) with Azure Machine Learning course is your gateway to mastering the complex world of deploying and managing AI models in the Azure cloud. This comprehensive learning path, designed for both data engineers and scientists, strengthens their MLOps skills to bridge the gap between experimentation and production, ensuring seamless AI lifecycle management.

Suitable Candidates

This course is ideal for 

• Data scientists and developers who are comfortable with Python or R and eager to streamline their ML workflows.

• ML practitioners who are seeking to apply DevOps principles for efficient model deployment.

• Azure enthusiasts willing to deepen their understanding of Azure Machine Learning tools and services.

Prerequisites

• Experience with Python or R programming languages for hands-on with real-world examples/exercises.

• Familiarity with machine learning model development and training.

• Basic understanding of Azure Machine Learning concepts and services.

Course Highlights

• Module 1 ‘Taking Models from Experimentation to Production’- Learn how to leverage Azure Machine Learning jobs to move your models from development to deployment.

• Module 2 ‘Automating ML Workflows with GitHub Actions’- Discover how to streamline your ML pipeline using GitHub Actions for automated training, testing, and deployment.

• Module 3 ‘Branch Protection and Workflow Triggering’- Master techniques for protecting your codebase and triggering ML workflows based on code changes.

• Module 4 ‘Automated Code Checks for ML Workloads’- Implement automated code checks to ensure quality and consistency in your ML projects.

• Module 5 ‘Environments for MLOps’- Understand how to use environments for model development, testing, and deployment as part of your MLOps strategy.

• Module 6 ‘Automating and Testing Model Deployment’- Learn how to automate and test model deployment with GitHub Actions and the Azure Machine Learning CLI (v2).

Skills Acquired

• Designing and implementing efficient MLOps pipelines on Azure.

• Automating ML workflows for faster model development and deployment.

• Leveraging GitHub Actions for robust CI/CD practices in ML projects.

• Implementing best practices for code version control and branch protection.

• Ensuring the quality and reliability of your ML models with automated code checks.

• Mastering environments for managing different stages of the ML lifecycle.

• Deploying and testing ML models automatically using GitHub Actions and Azure Machine Learning CLI.
  
  

Once you have achieved the End-to-End MLOps with Azure Machine Learning training, you will be ready to take the DP-100 Microsoft Azure Data Scientist Associate certification exam.

• DP-100: Microsoft Azure Data Scientist Associate Certification

The Microsoft Azure Data Scientist Associate (DP-100) certification is suitable for individuals who want to exhibit their expertise in applying data science and machine learning techniques on Microsoft Azure. It is suitable for data scientists, data analysts, and machine learning practitioners who work with Azure Machine Learning and want to validate their data science skills.

Source: Microsoft Azure Official Documentation

Prerequisites For The Certification Exam

Candidates aiming for the Microsoft Azure Data Scientist Associate (DP-100) certification should have practical experience in data science and machine learning. Familiarity with Azure services, Python programming, and statistical concepts will be beneficial if you want to become a Microsoft Certified Azure Data Scientist professional.

Skills/Knowledge Required

To succeed in the DP-100 exam and earn the Azure Data Scientist Associate certification, you should possess the following skills and knowledge-

• Practical experience in foundational data science and machine learning concepts.

• Familiarity with Python programming and its use in data science.

• Understanding of Azure services, particularly Azure Machine Learning.

• Knowledge of statistical concepts, data preparation, and feature engineering techniques.

Topics Covered In The Certification 

The Microsoft Azure Data Scientist Associate DP-100 certification covers various foundational concepts related to data science and machine learning on Azure, focusing on the following key areas-

• Data Preparation- Understanding data sources, cleaning, transformation, and performing feature engineering to prepare data for machine learning models.

• Model Training- Building and training machine learning models using Azure Machine Learning, including selecting algorithms, hyperparameter tuning, and model evaluation.

• Model Deployment And Monitoring- Deploying machine learning models as APIs and monitoring model performance over time.

• Machine Learning Interpretability- Interpreting and explaining machine learning model predictions.

Exam Details

• Exam Code- DP-100

• Exam Duration- 180 minutes (3 hours)

• Exam Format- The exam includes various question types, such as multiple-choice, multiple-response, and scenario-based questions.

• Exam Language- English, Japanese, Chinese (Simplified), Korean, German, Chinese (Traditional), French, Spanish, Portuguese (Brazil), Russian, Arabic (Saudi Arabia), Italian, Indonesian (Indonesia).

• Passing Score- The passing score may vary, typically around 700 out of 1000.

• Exam Fee And Registration Details

• The Microsoft Azure Data Scientist Associate DP-100 exam fee is $165. You can register for the DP-100 Microsoft Azure exam on the official Microsoft Certification website. 

3. GCP MLOps Certification

This section will help you begin your journey into GCP MLOps with the 'Machine Learning Operations: Getting Started' course, laying the foundation for advanced GCP MLOps practices. We will dive deeper into machine learning engineering by pursuing the GCP ML Engineer Professional Certification, marking your expertise in managing and deploying ML models on Google Cloud.

• Machine Learning Operations (MLOps): Getting Started

If you are intrigued by the intersection of machine learning and DevOps, GCP's 'Machine Learning Operations (MLOps) - Getting Started' course is your perfect launchpad. This beginner-friendly MLOps course equips you with the foundational knowledge and practical skills to gain a solid understanding of deploying and managing ML models in the Google Cloud ecosystem.

Suitable Candidates

This course is ideal for 

• Data scientists who want to streamline their journey from prototype to production, delivering impactful business results through MLOps practices.

• Software engineers looking to expand their skill set and dive into machine learning engineering on Google Cloud.

• ML engineers who want to enhance your expertise by adopting Google Cloud for their existing MLOps projects.

Prerequisites

• Basic understanding of software engineering and machine learning concepts and algorithms.

• Familiarity with cloud computing fundamentals (any cloud platform).

• No prior GCP experience is required.

Course Highlights

• Master Core MLOps Technologies- Identify and utilize essential tools like Vertex AI, Cloud Build, and Cloud Storage for effective ML model deployment and management.

• Employ CI/CD for ML- Integrate CI/CD best practices into your ML workflows, ensuring efficient and reliable model updates.

• Build Robust MLOps Environments- Configure and provision Google Cloud architectures for optimal performance and scalability in your MLOps projects.

• Hands-on Labs- Put your learning into practice by building and deploying your TensorFlow model on Vertex AI, gaining valuable experience with real-world examples.

Skills Acquired

• Identifying and utilizing core Google Cloud technologies for an effective MLOps infrastructure.

• Implementing best CI/CD practices for seamless ML model deployment and updates.

• Configuring and managing robust Google Cloud architectures for efficient MLOps workflows.

• Building and operating reliable and repeatable training and inference pipelines.
  
  

Once you have completed the MLOps: Getting Started course, you will be ready to take the GCP ML Engineer Professional certification exam.

• Google Cloud Certification: Machine Learning Engineer Professional Certificate

The Professional Machine Learning Engineer certification is ideal for individuals with machine learning expertise who are willing to showcase their ability to design, build, and deploy ML models on the Google Cloud Platform. This certification is intended for data scientists, machine learning engineers, and individuals developing machine learning solutions on GCP.

Source: Google Cloud Support Documentation

This certification exam assesses a candidate's proficiency in designing, building, and deploying ML models using GCP tools and services. It covers data preparation and preprocessing, model training and evaluation, model optimization and tuning, machine learning infrastructure, and developing machine learning models.

Prerequisites For The GCP ML Engineer Certification

Google recommends that candidates hold at least 3+ years of industry experience, including 1+ years of experience working on machine learning projects and familiarity with GCP machine learning services.

Skills/Knowledge Required

Candidates taking the Professional Machine Learning Engineer certification exam should deeply understand machine learning concepts and algorithms. They should be proficient in using GCP machine learning services such as AI Platform, AI Platform Notebooks, and AI Platform Training. They must possess knowledge of data engineering concepts, data preprocessing, and model evaluation techniques. Additionally, candidates should be familiar with deploying and managing ML models in a production environment.

Exam Details

• Exam Format- The GCP Professional Machine Learning Engineer certification exam comprises 50-60 multiple-choice and multiple-select questions. The total exam duration is 120 minutes (2 hours).

• Exam Language- The exam is available only in English.

• Exam Fee And Registration Details- The Professional Machine Learning Engineer Certification Fee is $200. Candidates could visit the official Google Cloud Platform certification website to register for this Google Cloud certification exam.

4. Databricks MLOps Certification

This section will help you explore the 'Machine Learning in Production Course' to gain hands-on insights into deploying models effectively. We will also understand how you can enhance your expertise further by aiming for the prestigious Databricks Certified ML Professional Certification. 

• Machine Learning in Production Course

Are you ready to bridge the gap between experimentation and real-world impact for your machine-learning models? Databricks' Machine Learning in Production Course is your comprehensive guide to mastering MLOps best practices on the Databricks platform. This course provides you the knowledge and skills to effectively build, deploy, and manage ML models in production, preparing you for the Databricks Certified Machine Learning Professional exam.

Suitable Candidates

This course is ideal for 

• Data scientists and ML engineers who want to expand their skills and confidently translate their models into real-world business value.

• This course offers a solid foundation and practical tools for anyone seeking to leverage Databricks to build data pipelines, whether you are a beginner or a seasoned practitioner.

• Anyone preparing for the Databricks Certified Machine Learning Professional exam who wants to gain the practical expertise and theoretical foundations to ace the exam confidently.

Prerequisites

• Intermediate Python Programming (Pandas) skills- A solid foundation in data manipulation is crucial. 

• Familiarity with Apache Spark- Understanding Spark's distributed processing capabilities is essential. 

• Working knowledge of Data Science and ML- Grasping core ML concepts ensures efficient model development.

Course Highlights

1. Feature Store and MLflow Mastery

• Master Databricks Feature Store for registering training data and ensuring data reproducibility.

• Leverage MLflow to track the ML lifecycle, package models for deployment, and manage models in production.

• Gain deep insights into model training and experimentation best practices.

2. Productionization and Monitoring

• Explore deployment paradigms for batch, streaming, and real-time scenarios.

• Build robust model monitoring solutions, including drift detection and anomaly identification.

• Implement CI/CD pipelines for automated model deployment and updates.

3. Hands-on Labs

• Apply your learning by building an end-to-end pipeline to log, deploy, and monitor ML models on Databricks.

• Gain practical experience solving real-world ML production system challenges.

Skills Acquired

• Designing and implementing efficient MLOps pipelines on Databricks.

• Leveraging Databricks Feature Store and MLflow for seamless model management.

• ML model monitoring and deployment for batch, streaming, and real-time applications.

• Implementing CI/CD pipelines for automated model updates and scaling.

• Confidently preparing for the Databricks Certified Machine Learning Professional exam.
  
  

Once you have completed the Machine Learning in Production course, you will be ready to take the Databricks Certified ML Professional certification exam.

• Databricks Certified ML Professional Certification

For experienced data scientists and ML engineers seeking to master the art of deploying and managing machine learning models on the Databricks platform, the Databricks Certified Machine Learning Professional Certification is the perfect recognition for you in the industry. This challenging exam validates your ability to handle the advanced tasks involved in real-world MLOps workflows, setting you apart as a leader in the field.

Source: Databricks

Suitable Candidates

This certification is ideal for 

• Data scientists and ML engineers with 1+ years of hands-on experience seeking to deepen their MLOps skillset.

• Professionals eager to showcase their proficiency in using Databricks Machine Learning for advanced production tasks.

Prerequisites

• While no formal prerequisites exist, taking the Databricks' ML in Production course is highly recommended for thorough preparation.

• 1+ years of hands-on experience performing the machine learning tasks outlined in the exam guide is expected.

Topics Covered In The Certification Exam

• Experimentation (30%)- Track, version, and manage ML experiments efficiently using Databricks tools.

• Model Lifecycle Management (30%)- Master the art of packaging, deploying, and managing ML models throughout their lifecycle.

• Model Deployment (25%)- Implement effective strategies for deploying models for batch, streaming, and real-time scenarios.

• Solution and Data Monitoring (15%)- Build robust monitoring solutions to detect data drift and ensure model performance.

Skills Acquired

• Designing and implementing advanced MLOps pipelines on Databricks.

• Employing Databricks tools like Feature Store and MLflow for seamless model management.

• Deploying and monitoring ML models for diverse use cases, including batch, streaming, and real-time scenarios.

• Building robust monitoring solutions to detect data drift and maintain model performance.

• Earn industry recognition and validate your expertise in Databricks Machine Learning.

Exam Details

• Exam Duration- 120 minutes (2 hours)

• Exam Format- The exam includes 60 multiple-choice questions.

• Exam Language- English.

• Exam Fee- The Databricks Certified Machine Learning Professional exam fee is $200, plus applicable taxes as required per local law.

Now that we have explored all the top MLOps certifications and training courses, it’s time to check out the best resources to help you become an MLOps expert.

MLOps Certification: Best Resources For Preparation

This section lists the best resources to help you master MLOps and prepare for your ideal MLOps certification exam.

1. MLOps Projects For Practice

You must practice the following MLOps projects to understand the real-world implementation of MLOps, including various modeling strategies, ML engineering, data lifecycle, and ML systems, using open-source tools and cloud platforms-

• ML Model Deployment on AWS for Customer Churn Prediction- Learn how to deploy a model on AWS that predicts whether the customer will churn soon or not.

• AWS MLOps Project to Deploy a Classification Model [Banking]- Learn how to deploy a classification model using Flask on AWS

• Azure Text Analytics for Medical Search Engine Deployment- Learn how to deploy an ML model into Azure Databricks using Azure text analytics cognitive service

• AWS Project to Build and Deploy LSTM Model with Sagemaker- Learn how to build an LSTM model on Sagemaker for sales forecasting.

Check out the MLOps Roadmap to access several MLOps projects to accelerate your MLOps journey.

2. YouTube Videos/Tutorials

Here are some top YouTube channels/videos you must explore to acquire MLOps certification free training-

• Databricks Tech Talks- Stay updated with industry trends and expert insights through live demos of Databricks and open source technologies such as Delta Lake, Spark, MLflow, etc., on Databricks' official YouTube channel.

• MLOps Community- This channel is linked with the MLOps Community Slack and presents the latest MLOps insights, best practices, challenges, and innovations. Featuring talks, interviews with top engineers, and weekly live streams, the channel also includes every episode of the MLOps Community podcast.

• DataTalksClub- This channel provides MLOps insights via Zoomcamps, hands-on workshops featuring popular MLOps tools, and highlights of open-source projects. Additionally, they host regular office hours and share interviews with MLOps practitioners.

3. Recommended Textbooks

Apart from video tutorials, you must explore the textbooks below to understand MLOps better-

• Introducing MLOps: How to Scale Machine Learning in the Enterprise by Mark Treveil- This book serves as a comprehensive guide to MLOps, authored by nine experts. It explicitly defines MLOps, addresses associated challenges, and provides essential insights for data science and ML professionals. Drawing on experience, the authors detail successful ML life cycle processes and offer practical guidance for their implementation in enterprise-scale workflows.

• Engineering MLOps by Emmanuel Raj- This book introduces fundamental MLOps concepts, guiding readers through the intersection of machine learning and software development workflows. The book offers practical insights into continuously training, deploying, and managing ML models, providing a hands-on experience for implementing MLOps practices in real-world organizational settings.

• Practical MLOps by Noah Gift and Alfredo Deza- This book takes you on a hands-on journey into operationalizing machine learning models. Gain actionable knowledge on ML development tools, operations methods, and their integration in MLOps. Read this book to learn how to implement these skills in real production environments using AWS, Azure, or GCP.

4. GitHub Repositories

Check out the following GitHub repositories to gain access to top MLOps resources-

• Awesome MLOps by Visenger

• Awesome MLOps Tools by kevins

• MLOps Examples by Microsoft

• MLOps Primer by dair-ai

5. Community Forums and Discussion Groups

Engaging with like-minded individuals is a powerful way to gain insights, tips, and support. Here are a few MLOps community forums and discussion groups you should engage with-

• Reddit's r/MLOps- Connect with other MLOps enthusiasts, share your experiences, and gain answers to crucial questions in a supportive community.

• Kaggle's MLOps Discussions- Learn from Kaggle's vibrant community of data scientists and ML engineers, engaging in discussions about real-world MLOps challenges.

• Slack's MLOps and Data Engineering Channels- Join various Slack channels dedicated to MLOps to network with professionals and access valuable resources.

Future-Proof Your Career With MLOps Certifications And ProjectPro

Earning an MLOps certification validates your skills, boosts your resume, and opens doors to exciting career opportunities. But knowledge without practice is of no use. That's where ProjectPro comes in. ProjectPro's end-to-end solved machine learning projects allow you to get your hands dirty building and deploying production-ready models, facing real-world challenges, and collaborating with industry experts. These projects are your portfolio, launchpad, and ticket to landing that dream MLOps job. 

So, why wait? Invest in yourself, invest in your future. Grab your MLOps certification, dive into the ProjectPro repository, and watch your career soar.